Effects of accelerated biological age on depressive symptoms in a causal reasoning framework.

BACKGROUND: Depression in middle-aged and elderly individuals is multifaceted and heterogeneous, linked to biological age (BA) based on aging-related biomarkers. However, due to confounding with chronological age and the absence of subgroup analysis and causal reasoning, the association between BA and depressive symptoms (DS) might be unstable and requires further investigation. METHODS: We utilized data from the China Health and Retirement Longitudinal Study (N = 9478) to perform association analysis, causal inference, and subgroup analysis. BA acceleration (BAA) was derived using machine learning and adjusted for chronological age. A generalized linear mixed-effects model (GLMM) tree algorithm was employed to identify subgroups. The causal reasoning frame included propensity score matching and fast large-scale almost matching exactly. RESULTS: In the longitudinal analysis, BAA exhibited a consistent and significant positive association with DS, even after controlling for demographic characteristics, lifestyle factors, health status, and physical functions. This association remained unchanged within the causal framework. GLMM tree analysis identified three partitioning variables (sex, satisfaction, and BMI) and five subgroups. Further subgroup analysis revealed that BAA exerted the strongest effect on DS among women with less satisfying lives. LIMITATIONS: Depressive symptoms were evaluated through scale measurements rather than clinical diagnosis. The sample was derived from the general population, not the clinically depressed population. CONCLUSIONS: This study provided the first longitudinal evidence that biological age acceleration increases depressive symptoms under causal reasoning and subgroup analysis, particularly among less satisfied women. And the association between BAA and DS was independent of known risk factors.

Dynamic heteroscedasticity of time series interpreted as complex networks.

Heteroscedasticity of time series is an important issue addressed in relation to the nonlinearity and complexity of time series. Previous studies have focused on time series heteroscedasticity during a long-term period but have rarely analyzed it from a nonlinear dynamic perspective. This paper proposes a new model for converting a time series into a complex network. Our proposed model can examine not only the heteroscedasticity of a short-term series but also the dynamic evolution process of this heteroscedasticity. Using four typical crude oil time series as sample data, we construct four networks. A network node denotes the types of fluctuation patterns corresponding to the symbolization of the heteroscedastic features of a short-term fluctuation series based on the autoregressive generalized autoregressive conditional heteroscedasticity model, and a weighted edge represents the evolution direction and frequency between two patterns. Our findings show that the choice of the length of a short-term period depends on the diversity of these patterns. The identification of the nodes with greater out-strength or greater betweenness centrality can help us to understand the different roles of fluctuation patterns in the evolution process. We propose a method for predicting the most probable target nodes from a source node. The analysis of clustering effects can help in detecting the fluctuation patterns between different clusters. This paper investigates the evolution dynamic mechanism of the heteroscedastic features of a short-term time series, which can help researchers and investors deeply understand the dynamic process of time series.